Understanding the Nature of Interprofessional Collaboration and Patient Family Involvement in Intensive Care Settings
Bibliographic record
Abstract
Although effective interprofessional collaboration is a key component of patient safety and quality improvement initiatives, little is known about the nature of collaboration in ICU settings. Through ethnographic research, this study will explore interprofessional care in 8 ICUs (6 based in the United States and 2 based in Canada), develop an empirically based readiness/diagnostic tool to assess the quality of team-based care delivery, and develop interventions to strengthen team-based care and patient family involvement. Our study has 3 iterative phases and will involve: a scoping review of the literature on team dynamics in the ICU, an ethnographic study (observation, shadowing, interviews) across 8 sites over 2 years and the collection of clinical outcomes data to inform the development of a “diagnostics” tool for interprofessional collaboration and family member involvement in ICU care, as well as interprofessional intervention development. The importance of ethnographic and other forms of qualitative research for the improvement of health care delivery has already been recognized. This study’s comparative design and the richness of its data have the potential to generate a multidimensional understanding of the processes of interprofessional collaboration and patient family member involvement. The creation of generally applicable, empirically grounded tools also has the potential to enhance these processes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".